MoonshotAI
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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它扩展了Karpathy的LLM Wiki模式，增加了明确出处角色、已发布产品层、AI输出暂存区和类型化的机器可读链接，同时保持Obsidian和Codex为可选界面，将仓库本身作为持久系统。
适合谁使用
- 需要将个人想法与外部证据区分开的研究人员
- 使用AI辅助创作并管理引用的作家和出版人
- 开发课程材料并需要审查流程的教师
- 希望使用Codex构建结构化知识库的AI高级用户
典型使用场景
- 带出处信息地捕获和索引个人笔记与外部来源
- 从知识库中创作文章或课程，包括来源规划和引用
- 将现有Obsidian知识库迁移为审查优先、Agent友好的结构
- 查询知识图谱以找到原始来源和关系
项目优势
- 通过专用目录清晰区分原始输入、外部来源、已发布输出和Wiki概念
- 分阶段审查生命周期防止AI生成的文本未经人工批准成为事实
- .kb/links/中的类型化YAML侧边文件提供可独立查询的机器可读图
- 使用纯Markdown，可选Obsidian或Codex，无需绑定特定工具
使用前须知
- 自动化的功能极简，没有内置搜索、嵌入或图数据库，直到真实查询证明需要
- 每次从暂存区提升到Wiki或产品都需要持续的人工审查
- Obsidian和Codex是可选但工作流围绕它们设计；仅使用纯Markdown会失去部分可视化和Agent集成能力
README 快速开始
AI Content Knowledge Base A review-first, AI-native knowledge base for people who research, write, teach, and publish. Markdown + Codex + Obsidian + typed YAML relationships
English · 简体中文
[!NOTE] This project was inspired by Andrej Karpathy's LLM Wiki pattern: immutable raw sources, a persistent and compounding Markdown wiki maintained by an LLM, and a repository schema such as
AGENTS.mdthat defines how the agent should ingest, query, and maintain knowledge.
ai-content-kbextends that idea with explicit provenance roles, a first-class published-product layer, review-first staging, typed machine-readable relationships, content-creation workflows, and safe legacy-vault migration.
Most knowledge bases mix personal thinking, external evidence, published work, and AI-generated text in the same pile. This template gives each a clear role—and gives AI agents rules for working without silently polluting originals or publication workflows.
Originals stay trustworthy. The wiki stays readable. The graph stays queryable. AI output stays reviewable.
The screenshot shows a mature vault using this architecture. A fresh clone starts with a small synthetic example and grows as you add reviewed notes, links, and tags.
Why this project
| Need | How this project handles it |
|---|---|
| Keep personal ideas distinct from external claims | Separate raw/ and sources/ provenance layers |
| Reuse published articles, courses, and scripts | Treat reviewed output as a first-class products/ layer |
| Let people browse without turning summaries into proof | Use wiki/ as a cited human interface |
| Give agents precise relationships | Store typed, evidenced YAML sidecars in .kb/links/ |
| Prevent generated text from becoming truth by accident | Route AI output through .kb/staging/ and human review |
| Work naturally with Codex | Ship durable workflows in root AGENTS.md |
| Keep Obsidian optional | Store everything as ordinary Markdown, YAML, JSON, and folders |
Quickstart
git clone https://github.com/mrbear1024/ai-content-kb.git
cd ai-content-kb
No application is required to read the vault. Use plain Markdown, open the root as an Obsidian vault, or use the intended Codex workflow below.
Open in Code
项目描述
A review-first reference architecture for AI-assisted personal content knowledge systems
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